Anomaly Detection and Prototype Selection Using Polyhedron Curvature
University of Waterloo（滑铁卢大学）
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
备注：
Accepted (to appear) in Canadian Conference on Artificial Intelligence (Canadian AI conference) 2020, Springer. This version includes supplementary material for derivation of an equation
发表信息：
Canadian Conference on Artificial Intelligence, pp. 238-250. Springer, Cham, 2020
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for a point, we consider every data point as the vertex of a polyhedron where the more anomalous point has more curvature. We also propose inverse CAD (iCAD) and Kernel iCAD for instance ranking and prototype selection by looking at CAD from an opposite perspective. We define the concept of anomaly landscape and anomaly path and we demonstrate an application for it which is image denoising. The proposed methods are straightforward and easy to implement. Our experiments on different benchmarks show that the proposed methods are effective for anomaly detection and prototype selection.
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
School of Software and Microelectronics Peking University（北京大学软件与微电子学院）；School of Computer Science Peking University（北京大学计算机学院）；National Engineering Center of Software Engineering Peking University（北京大学软件工程国家工程研究中心）
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）
Generalist Graph Anomaly Detection via Prototype-Based Distillation
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China（西安交通大学计算机科学与技术学院）；National Engineering Research Center for Visual Information and Applications, Xi'an, China（视觉信息与应用国家工程研究中心）；University of Virginia, Charlottesville, USA（弗吉尼亚大学）；University of Central Florida, Orlando, USA（佛罗里达大学）；School of Distance Education, Xi’an Jiaotong University, Xi'an, China（西安交通大学继续教育学院）；School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China（西安交通大学网络安全学院）
DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
Department of Computing（计算系）；The Hong Kong Polytechnic University（香港理工大学）；Department of Computer Science（计算机科学系）；City University of Hong Kong（城市大学）；School of Software（软件学院）；Beihang University（北航）；University of Illinons at Chicago（伊利诺伊大学香槟分校）
EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts
Qilu Hospital, ASU（青岛医院，亚利桑那州立大学）；Zhengzhou University（郑州大学）；University of Science and Technology Beijing（北京科技大学）；Southern University of Science and Technology（南方科技大学）；Qilu Hospital（青岛医院）
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Institute of Technology（北京理工大学）；Jinan University（暨南大学）；Southeast University（东南大学）
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与扩展现实研究所）
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；China Mobile (Jiangxi) Virtual Reality Technology Co., Ltd.（中国移动（江西）虚拟现实技术有限公司）；Salesforce AI Research（Salesforce AI研究）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Learning Citywide Patterns of Life from Trajectory Monitoring
Novateur Research Solutions（诺瓦图研究解决方案公司）
最近，真实世界人类移动数据集的激增促进了轨迹预测、需求预测、旅行时间估计和异常检测等方面的地理空间和交通研究。然而，这些数据集也可以更广泛地对复杂的人类流动系统进行描述性分析。我们将生命分析模式正式定义为在线无监督异常检测的自然，可解释的扩展，我们不仅监控数据流的异常，还明确提取正常模式。为了学习生活模式，我们将计算生物学和神经机器人学研究中的“需要时生长”（Grow When Required，GWR）情景记忆应用到地理空间分析的新领域。这种受生物启发的神经网络与自组织映射（SOM）相关，当它在GPS流上迭代时，会逐步构建一组“记忆”或原型交通模式。然后，它将每个新的观察结果与之前的经验进行比较，从而对数据进行在线、无监督的聚类和异常检测。我们从波尔图出租车数据集中挖掘感兴趣的模式，包括主要的公共假日和新发现的交通异常，如节日和音乐会，据我们所知，这些都是以前没有被承认或报告的。我们预计，逐步学习正常和异常道路交通行为的能力将在许多领域有用，包括智能城市、自动驾驶汽车以及城市规划和管理。
Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC
LIBPhys, Department of Physics, University of Coimbra（科英布拉大学物理系LIBPhys部门）；Dipartimento di Matematica e Fisica, Università Roma Tre（罗马三大学数学与物理系）；INFN Sezione di Roma Tre（罗马三大学国家核物理研究所）；Gran Sasso Science Institute（格兰萨索科学研究所）；INFN Laboratori Nazionali del Gran Sasso（格兰萨索国家实验室）；INFN Laboratori Nazionali di Frascati（弗拉斯卡蒂国家实验室）；ENEA Centro Ricerche Frascati（弗拉斯卡蒂能源研究所）；Universidade Federal de Juiz de Fora, Faculdade de Engenharia（贝洛奥预托联邦大学工程学院）；Dipartimento di Fisica, Sapienza Università di Roma（罗马萨皮恩扎大学物理系）；INFN Sezione di Roma（罗马国家核物理研究所）；Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学）；Dipartimento di Ingegneria Chimica, Materiali e Ambiente, Sapienza Università di Roma（罗马萨皮恩扎大学化学工程与材料环境系）；Department of Physics and Astronomy, University of Sheffield（谢菲尔德大学物理与天文学系）
Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection
The Hong Kong Polytechnic University（香港理工大学）；Institute of Information Processing and Automation, Zhejiang University of Technology（浙江工业大学信息处理与自动化研究所）；University of Illinois at Chicago (UIC)（伊利诺伊大学芝加哥分校）
A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
New York University, NYUAD, Division of Science, Computer Science Department（纽约大学，NYUAD，科学学院，计算机科学系）；University of Quebec in Montreal, Computer Science Department, Montreal（魁北克大学蒙特利尔分校，计算机科学系，蒙特利尔）；University of Edinburgh, School of Informatics, Edinburgh（爱丁堡大学，信息学院，爱丁堡）
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality
School of Computer Science Northwestern Polytechnical University Xi'an, China；School of Electrical Engineering \& Computer Science University of Electronic Science；School of Electrical Engineering \& Computer Science The University of Queensland Brisbane, Australia
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Centre of Informatics and Systems of the University of Coimbra, CISUC（科英布拉大学信息与系统中心）；University of Coimbra（科英布拉大学）；Institute for Systems Engineering and Computers, INESC（系统工程与计算机研究所）；Sul-Rio-Grandense Federal Institute (IFSUL)（南里奥格兰德联邦学院）；Instituto de Telecomunicações（电信研究所）；Polytechnic Institute of Coimbra（科英布拉理工学院）；Coimbra Business School（科英布拉商学院）；INESC Coimbra（科英布拉INESC）
